Is it possible to flip the features and labels after training a model?
The body of your post seems to be asking a completely separate question than the title of your post, so I will answer both:
&quot;Body: How do I complete the goal of this program?&quot;
Your dataset does not have the dependent variable, which is the outcome of the game (win/loss/draw). What I am assuming is that you have a way of looking up the outcome of the game from either the &quot;id&quot; field or the &quot;gameReferenceId&quot; field.
So you would have to augment the dataset with a new column, &quot;gameOutcome&quot;, which has values (win/loss/draw), by looking up the outcome of each game (each row) and adding that to your dataset.
Once you have this, you have the 12 independent variables (the 12 columns already there), and the 1 dependent variable (the &quot;gameOutcome&quot;), and the prediction task should be straightforward from there.
&quot;Title: Given a label, how do I predict features?&quot;
(note: this section will not help your program)
What you are looking for are generative models. Generative models can generate instances given the label and/or some random seed. This is a completely different model than discriminative models, which given the instance predicts the label (this is the one you have).
The simplest generative model is a Naive Bayes model. While Naive Bayes is normally used as a discriminative model (to classify a label), it has enough information to generate instances given a label as well. Here are some tips on how to turn Naive Bayes into a generative model.
If you are looking for generative deep neural network models, this blog by OpenAI has a nice overview explaining the three modern approaches (generative adversarial networks, variational autoencoders, and autoregressive models). The blog explains at a high level what the strengths and weaknesses are of each approach, and links to some open source projects for you to try it out as well.